Testing onnx in new ui updates

invokeai/app/invocations/onnx.py

@@ -430,3 +430,118 @@ class ONNXSD1ModelLoaderInvocation(BaseInvocation):
                 ),
             )
         )
+
+class OnnxModelField(BaseModel):
+    """Onnx model field"""
+
+    model_name: str = Field(description="Name of the model")
+    base_model: BaseModelType = Field(description="Base model")
+
+class OnnxModelLoaderInvocation(BaseInvocation):
+    """Loads a main model, outputting its submodels."""
+
+    type: Literal["onnx_model_loader"] = "onnx_model_loader"
+
+    model: OnnxModelField = Field(description="The model to load")
+    # TODO: precision?
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "title": "Onnx Model Loader",
+                "tags": ["model", "loader"],
+                "type_hints": {"model": "model"},
+            },
+        }
+
+    def invoke(self, context: InvocationContext) -> ONNXModelLoaderOutput:
+        base_model = self.model.base_model
+        model_name = self.model.model_name
+        model_type = ModelType.ONNX
+
+        # TODO: not found exceptions
+        if not context.services.model_manager.model_exists(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+        ):
+            raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
+
+        """
+        if not context.services.model_manager.model_exists(
+            model_name=self.model_name,
+            model_type=SDModelType.Diffusers,
+            submodel=SDModelType.Tokenizer,
+        ):
+            raise Exception(
+                f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted"
+            )
+
+        if not context.services.model_manager.model_exists(
+            model_name=self.model_name,
+            model_type=SDModelType.Diffusers,
+            submodel=SDModelType.TextEncoder,
+        ):
+            raise Exception(
+                f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted"
+            )
+
+        if not context.services.model_manager.model_exists(
+            model_name=self.model_name,
+            model_type=SDModelType.Diffusers,
+            submodel=SDModelType.UNet,
+        ):
+            raise Exception(
+                f"Failed to find unet submodel from {self.model_name}! Check if model corrupted"
+            )
+        """
+
+        return ONNXModelLoaderOutput(
+            unet=UNetField(
+                unet=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.UNet,
+                ),
+                scheduler=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Scheduler,
+                ),
+                loras=[],
+            ),
+            clip=ClipField(
+                tokenizer=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer,
+                ),
+                text_encoder=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder,
+                ),
+                loras=[],
+            ),
+            vae_decoder=VaeField(
+                vae=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.VaeDecoder,
+                ),
+            ),
+            vae_encoder=VaeField(
+                vae=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.VaeEncoder,
+                ),
+            )
+        )

invokeai/backend/model_management/model_probe.py

@@ -22,7 +22,7 @@ class ModelProbeInfo(object):
     variant_type: ModelVariantType
     prediction_type: SchedulerPredictionType
     upcast_attention: bool
-    format: Literal['diffusers','checkpoint', 'lycoris']
+    format: Literal['diffusers','checkpoint', 'lycoris', 'olive']
     image_size: int
 
 class ProbeBase(object):

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-f28934f1.js

@@ -1,4 +1,4 @@
-import{v as S,ga as Ze,q as k,M as Et,gb as Dt,ae as bt,ag as c,gc as v,gd as jt,ge as a,gf as Rt,gg as p,gh as vt,gi as Ht,gj as Wt,aX as Vt,gk as Lt,Z as Ot,gl as qt,gm as Nt,gn as Gt,go as Ut,aV as Xt}from"./index-078526aa.js";import{M as Yt}from"./MantineProvider-8988d217.js";var ut=String.raw,ft=ut`
+import{v as S,gn as Ze,q as k,M as Et,go as Dt,ae as bt,ag as c,gp as v,gq as jt,gr as a,gs as Rt,gt as p,gu as vt,gv as Ht,gw as Wt,aX as Vt,gx as Lt,Z as Ot,gy as qt,gz as Nt,gA as Gt,gB as Ut,aV as Xt}from"./index-63d3e2f4.js";import{M as Yt}from"./MantineProvider-ffc3d7ba.js";var ut=String.raw,ft=ut`
   :root,
   :host {
     --chakra-vh: 100vh;

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-078526aa.js"></script>
+    <script type="module" crossorigin src="./assets/index-63d3e2f4.js"></script>
   </head>
 
   <body dir="ltr">

invokeai/frontend/web/dist/locales/en.json

@@ -102,7 +102,8 @@
         "openInNewTab": "Open in New Tab",
         "dontAskMeAgain": "Don't ask me again",
         "areYouSure": "Are you sure?",
-        "imagePrompt": "Image Prompt"
+        "imagePrompt": "Image Prompt",
+        "clearNodes": "Are you sure you want to clear all nodes?"
     },
     "gallery": {
         "generations": "Generations",
@@ -528,7 +529,7 @@
         "hidePreview": "Hide Preview",
         "showPreview": "Show Preview",
         "controlNetControlMode": "Control Mode",
-        "clipSkip": "Clip Skip",
+        "clipSkip": "CLIP Skip",
         "aspectRatio": "Ratio"
     },
     "settings": {
@@ -593,7 +594,11 @@
         "metadataLoadFailed": "Failed to load metadata",
         "initialImageSet": "Initial Image Set",
         "initialImageNotSet": "Initial Image Not Set",
-        "initialImageNotSetDesc": "Could not load initial image"
+        "initialImageNotSetDesc": "Could not load initial image",
+        "nodesSaved": "Nodes Saved",
+        "nodesLoaded": "Nodes Loaded",
+        "nodesLoadedFailed": "Failed To Load Nodes",
+        "nodesCleared": "Nodes Cleared"
     },
     "tooltip": {
         "feature": {
@@ -674,5 +679,11 @@
         "showProgressImages": "Show Progress Images",
         "hideProgressImages": "Hide Progress Images",
         "swapSizes": "Swap Sizes"
+    },
+    "nodes": {
+        "reloadSchema": "Reload Schema",
+        "saveNodes": "Save Nodes",
+        "loadNodes": "Load Nodes",
+        "clearNodes": "Clear Nodes"
     }
 }

invokeai/frontend/web/src/features/nodes/components/fields/ModelInputFieldComponent.tsx

@@ -11,7 +11,10 @@ import { MODEL_TYPE_MAP as BASE_MODEL_NAME_MAP } from 'features/system/component
 import { forEach, isString } from 'lodash-es';
 import { memo, useCallback, useEffect, useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
-import { useGetMainModelsQuery } from 'services/api/endpoints/models';
+import {
+  useGetMainModelsQuery,
+  useGetOnnxModelsQuery,
+} from 'services/api/endpoints/models';
 import { FieldComponentProps } from './types';
 
 const ModelInputFieldComponent = (
@@ -23,6 +26,7 @@ const ModelInputFieldComponent = (
   const { t } = useTranslation();
 
   const { data: mainModels } = useGetMainModelsQuery();
+  const { data: onnxModels } = useGetOnnxModelsQuery();
 
   const data = useMemo(() => {
     if (!mainModels) {
@@ -43,12 +47,33 @@ const ModelInputFieldComponent = (
       });
     });
 
+    if (onnxModels) {
+      forEach(onnxModels.entities, (model, id) => {
+        if (!model) {
+          return;
+        }
+
+        data.push({
+          value: id,
+          label: model.model_name,
+          group: BASE_MODEL_NAME_MAP[model.base_model],
+        });
+      });
+    }
     return data;
-  }, [mainModels]);
+  }, [mainModels, onnxModels]);
 
   const selectedModel = useMemo(
-    () => mainModels?.entities[field.value ?? mainModels.ids[0]],
+    () =>
-    [mainModels?.entities, mainModels?.ids, field.value]
+      mainModels?.entities[field.value ?? mainModels.ids[0]] ||
+      onnxModels?.entities[field.value ?? onnxModels.ids[0]],
+    [
+      mainModels?.entities,
+      mainModels?.ids,
+      onnxModels?.entities,
+      onnxModels?.ids,
+      field.value,
+    ]
   );
 
   const handleValueChanged = useCallback(
@@ -69,18 +94,22 @@ const ModelInputFieldComponent = (
   );
 
   useEffect(() => {
-    if (field.value && mainModels?.ids.includes(field.value)) {
+    if (
+      field.value &&
+      (mainModels?.ids.includes(field.value) ||
+        onnxModels?.ids.includes(field.value))
+    ) {
       return;
     }
 
-    const firstModel = mainModels?.ids[0];
+    const firstModel = mainModels?.ids[0] || onnxModels?.ids[0];
 
     if (!isString(firstModel)) {
       return;
     }
 
     handleValueChanged(firstModel);
-  }, [field.value, handleValueChanged, mainModels?.ids]);
+  }, [field.value, handleValueChanged, mainModels?.ids, onnxModels?.ids]);
 
   return (
     <IAIMantineSelect

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addLoRAsToGraph.ts

@@ -10,6 +10,7 @@ import {
   CLIP_SKIP,
   LORA_LOADER,
   MAIN_MODEL_LOADER,
+  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   POSITIVE_CONDITIONING,
@@ -41,6 +42,10 @@ export const addLoRAsToGraph = (
         !(
           e.source.node_id === MAIN_MODEL_LOADER &&
           ['unet'].includes(e.source.field)
+        ) &&
+        !(
+          e.source.node_id === ONNX_MODEL_LOADER &&
+          ['unet'].includes(e.source.field)
         )
     );
     // Remove CLIP_SKIP connections to conditionings to feed it through LoRAs
@@ -77,11 +82,14 @@ export const addLoRAsToGraph = (
     // add to graph
     graph.nodes[currentLoraNodeId] = loraLoaderNode;
 
+    const model_loader = id.includes('onnx')
+      ? ONNX_MODEL_LOADER
+      : MAIN_MODEL_LOADER;
     if (currentLoraIndex === 0) {
       // first lora = start the lora chain, attach directly to model loader
       graph.edges.push({
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'unet',
         },
         destination: {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addVAEToGraph.ts

@@ -10,6 +10,7 @@ import {
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
   METADATA_ACCUMULATOR,
+  ONNX_MODEL_LOADER,
   TEXT_TO_IMAGE_GRAPH,
   VAE_LOADER,
 } from './constants';
@@ -33,11 +34,13 @@ export const addVAEToGraph = (
       vae_model,
     };
   }
-
+  const model_loader = vae?.id.includes('onnx')
+    ? ONNX_MODEL_LOADER
+    : MAIN_MODEL_LOADER;
   if (graph.id === TEXT_TO_IMAGE_GRAPH || graph.id === IMAGE_TO_IMAGE_GRAPH) {
     graph.edges.push({
       source: {
-        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        node_id: isAutoVae ? model_loader : VAE_LOADER,
         field: 'vae',
       },
       destination: {
@@ -50,7 +53,7 @@ export const addVAEToGraph = (
   if (graph.id === IMAGE_TO_IMAGE_GRAPH) {
     graph.edges.push({
       source: {
-        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        node_id: isAutoVae ? model_loader : VAE_LOADER,
         field: 'vae',
       },
       destination: {
@@ -63,7 +66,7 @@ export const addVAEToGraph = (
   if (graph.id === INPAINT_GRAPH) {
     graph.edges.push({
       source: {
-        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        node_id: isAutoVae ? model_loader : VAE_LOADER,
         field: 'vae',
       },
       destination: {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasImageToImageGraph.ts

@@ -18,6 +18,7 @@ import {
   LATENTS_TO_IMAGE,
   LATENTS_TO_LATENTS,
   MAIN_MODEL_LOADER,
+  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
@@ -59,6 +60,11 @@ export const buildCanvasImageToImageGraph = (
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
 
+  console.log(model);
+  const model_loader = model.model_name.includes('onnx')
+    ? ONNX_MODEL_LOADER
+    : MAIN_MODEL_LOADER;
+
   /**
    * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
    * full graph here as a template. Then use the parameters from app state and set friendlier node
@@ -69,6 +75,7 @@ export const buildCanvasImageToImageGraph = (
    */
 
   // copy-pasted graph from node editor, filled in with state values & friendly node ids
+  // TODO: Actually create the graph correctly for ONNX
   const graph: NonNullableGraph = {
     id: IMAGE_TO_IMAGE_GRAPH,
     nodes: {
@@ -87,9 +94,9 @@ export const buildCanvasImageToImageGraph = (
         id: NOISE,
         use_cpu,
       },
-      [MAIN_MODEL_LOADER]: {
+      [model_loader]: {
-        type: 'main_model_loader',
+        type: model_loader,
-        id: MAIN_MODEL_LOADER,
+        id: model_loader,
         model,
       },
       [CLIP_SKIP]: {
@@ -121,7 +128,7 @@ export const buildCanvasImageToImageGraph = (
     edges: [
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'clip',
         },
         destination: {
@@ -181,7 +188,7 @@ export const buildCanvasImageToImageGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'unet',
         },
         destination: {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasInpaintGraph.ts

@@ -15,6 +15,7 @@ import {
   INPAINT_GRAPH,
   ITERATE,
   MAIN_MODEL_LOADER,
+  ONNX_MODEL_LOADER,
   NEGATIVE_CONDITIONING,
   POSITIVE_CONDITIONING,
   RANDOM_INT,
@@ -63,6 +64,12 @@ export const buildCanvasInpaintGraph = (
   // We may need to set the inpaint width and height to scale the image
   const { scaledBoundingBoxDimensions, boundingBoxScaleMethod } = state.canvas;
 
+  console.log(model);
+  const model_loader = model.model_name.includes('onnx')
+    ? ONNX_MODEL_LOADER
+    : MAIN_MODEL_LOADER;
+
+  // TODO: Actually create the graph correctly for ONNX
   const graph: NonNullableGraph = {
     id: INPAINT_GRAPH,
     nodes: {
@@ -107,9 +114,9 @@ export const buildCanvasInpaintGraph = (
         id: NEGATIVE_CONDITIONING,
         prompt: negativePrompt,
       },
-      [MAIN_MODEL_LOADER]: {
+      [model_loader]: {
-        type: 'main_model_loader',
+        type: model_loader,
-        id: MAIN_MODEL_LOADER,
+        id: model_loader,
         model,
       },
       [CLIP_SKIP]: {
@@ -133,7 +140,7 @@ export const buildCanvasInpaintGraph = (
     edges: [
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'unet',
         },
         destination: {
@@ -143,7 +150,7 @@ export const buildCanvasInpaintGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'clip',
         },
         destination: {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildCanvasTextToImageGraph.ts

@@ -10,6 +10,7 @@ import {
   CLIP_SKIP,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
+  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
@@ -49,7 +50,10 @@ export const buildCanvasTextToImageGraph = (
   const use_cpu = shouldUseNoiseSettings
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
-
+  console.log(model);
+  const model_loader = model.model_name.includes('onnx')
+    ? ONNX_MODEL_LOADER
+    : MAIN_MODEL_LOADER;
   /**
    * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
    * full graph here as a template. Then use the parameters from app state and set friendlier node
@@ -60,6 +64,7 @@ export const buildCanvasTextToImageGraph = (
    */
 
   // copy-pasted graph from node editor, filled in with state values & friendly node ids
+  // TODO: Actually create the graph correctly for ONNX
   const graph: NonNullableGraph = {
     id: TEXT_TO_IMAGE_GRAPH,
     nodes: {
@@ -87,9 +92,9 @@ export const buildCanvasTextToImageGraph = (
         scheduler,
         steps,
       },
-      [MAIN_MODEL_LOADER]: {
+      [model_loader]: {
-        type: 'main_model_loader',
+        type: model_loader,
-        id: MAIN_MODEL_LOADER,
+        id: model_loader,
         model,
       },
       [CLIP_SKIP]: {
@@ -125,7 +130,7 @@ export const buildCanvasTextToImageGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'clip',
         },
         destination: {
@@ -155,7 +160,7 @@ export const buildCanvasTextToImageGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'unet',
         },
         destination: {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearImageToImageGraph.ts

@@ -17,6 +17,7 @@ import {
   LATENTS_TO_IMAGE,
   LATENTS_TO_LATENTS,
   MAIN_MODEL_LOADER,
+  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
@@ -82,13 +83,19 @@ export const buildLinearImageToImageGraph = (
     ? shouldUseCpuNoise
     : initialGenerationState.shouldUseCpuNoise;
 
+  console.log(model);
+  const model_loader = model.model_name.includes('onnx')
+    ? ONNX_MODEL_LOADER
+    : MAIN_MODEL_LOADER;
+
   // copy-pasted graph from node editor, filled in with state values & friendly node ids
+  // TODO: Actually create the graph correctly for ONNX
   const graph: NonNullableGraph = {
     id: IMAGE_TO_IMAGE_GRAPH,
     nodes: {
-      [MAIN_MODEL_LOADER]: {
+      [model_loader]: {
-        type: 'main_model_loader',
+        type: model_loader,
-        id: MAIN_MODEL_LOADER,
+        id: model_loader,
         model,
       },
       [CLIP_SKIP]: {
@@ -135,7 +142,7 @@ export const buildLinearImageToImageGraph = (
     edges: [
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'unet',
         },
         destination: {
@@ -145,7 +152,7 @@ export const buildLinearImageToImageGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'clip',
         },
         destination: {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/buildLinearTextToImageGraph.ts

@@ -10,6 +10,7 @@ import {
   CLIP_SKIP,
   LATENTS_TO_IMAGE,
   MAIN_MODEL_LOADER,
+  ONNX_MODEL_LOADER,
   METADATA_ACCUMULATOR,
   NEGATIVE_CONDITIONING,
   NOISE,
@@ -46,6 +47,10 @@ export const buildLinearTextToImageGraph = (
     throw new Error('No model found in state');
   }
 
+  console.log(model);
+  const model_loader = model.model_name.includes('onnx')
+    ? ONNX_MODEL_LOADER
+    : MAIN_MODEL_LOADER;
   /**
    * The easiest way to build linear graphs is to do it in the node editor, then copy and paste the
    * full graph here as a template. Then use the parameters from app state and set friendlier node
@@ -56,12 +61,14 @@ export const buildLinearTextToImageGraph = (
    */
 
   // copy-pasted graph from node editor, filled in with state values & friendly node ids
+
+  // TODO: Actually create the graph correctly for ONNX
   const graph: NonNullableGraph = {
     id: TEXT_TO_IMAGE_GRAPH,
     nodes: {
-      [MAIN_MODEL_LOADER]: {
+      [model_loader]: {
-        type: 'main_model_loader',
+        type: model_loader,
-        id: MAIN_MODEL_LOADER,
+        id: model_loader,
         model,
       },
       [CLIP_SKIP]: {
@@ -101,7 +108,7 @@ export const buildLinearTextToImageGraph = (
     edges: [
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'clip',
         },
         destination: {
@@ -111,7 +118,7 @@ export const buildLinearTextToImageGraph = (
       },
       {
         source: {
-          node_id: MAIN_MODEL_LOADER,
+          node_id: model_loader,
           field: 'unet',
         },
         destination: {

invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts

@@ -8,6 +8,7 @@ export const RANDOM_INT = 'rand_int';
 export const RANGE_OF_SIZE = 'range_of_size';
 export const ITERATE = 'iterate';
 export const MAIN_MODEL_LOADER = 'main_model_loader';
+export const ONNX_MODEL_LOADER = 'onnx_model_loader';
 export const VAE_LOADER = 'vae_loader';
 export const LORA_LOADER = 'lora_loader';
 export const CLIP_SKIP = 'clip_skip';

invokeai/frontend/web/src/features/nodes/util/modelIdToOnnxModelField.ts

@@ -0,0 +1,16 @@
+import { BaseModelType, OnnxModelField } from 'services/api/types';
+
+/**
+ * Crudely converts a model id to a main model field
+ * TODO: Make better
+ */
+export const modelIdToOnnxModelField = (modelId: string): OnnxModelField => {
+  const [base_model, model_type, model_name] = modelId.split('/');
+
+  const field: OnnxModelField = {
+    base_model: base_model as BaseModelType,
+    model_name,
+  };
+
+  return field;
+};

invokeai/frontend/web/src/features/parameters/store/actions.ts

@@ -1,10 +1,10 @@
 import { createAction } from '@reduxjs/toolkit';
-import { ImageDTO, MainModelField } from 'services/api/types';
+import { ImageDTO, MainModelField, OnnxModelField } from 'services/api/types';
 
 export const initialImageSelected = createAction<ImageDTO | string | undefined>(
   'generation/initialImageSelected'
 );
 
-export const modelSelected = createAction<MainModelField>(
+export const modelSelected = createAction<MainModelField | OnnxModelField>(
   'generation/modelSelected'
 );

invokeai/frontend/web/src/features/parameters/store/generationSlice.ts

@@ -8,7 +8,7 @@ import {
   setShouldShowAdvancedOptions,
 } from 'features/ui/store/uiSlice';
 import { clamp } from 'lodash-es';
-import { ImageDTO, MainModelField } from 'services/api/types';
+import { ImageDTO, MainModelField, OnnxModelField } from 'services/api/types';
 import { clipSkipMap } from '../components/Parameters/Advanced/ParamClipSkip';
 import {
   CfgScaleParam,
@@ -53,7 +53,7 @@ export interface GenerationState {
   shouldUseSymmetry: boolean;
   horizontalSymmetrySteps: number;
   verticalSymmetrySteps: number;
-  model: MainModelField | null;
+  model: MainModelField | OnnxModelField | null;
   vae: VaeModelParam | null;
   seamlessXAxis: boolean;
   seamlessYAxis: boolean;
@@ -226,7 +226,10 @@ export const generationSlice = createSlice({
       const { image_name, width, height } = action.payload;
       state.initialImage = { imageName: image_name, width, height };
     },
-    modelChanged: (state, action: PayloadAction<MainModelField | null>) => {
+    modelChanged: (
+      state,
+      action: PayloadAction<MainModelField | OnnxModelField | null>
+    ) => {
       if (!action.payload) {
         state.model = null;
       }

invokeai/frontend/web/src/features/system/components/ModelSelect.tsx

@@ -11,7 +11,11 @@ import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
 import { modelIdToMainModelField } from 'features/nodes/util/modelIdToMainModelField';
 import { modelSelected } from 'features/parameters/store/actions';
 import { forEach } from 'lodash-es';
-import { useGetMainModelsQuery } from 'services/api/endpoints/models';
+import {
+  useGetMainModelsQuery,
+  useGetOnnxModelsQuery,
+} from 'services/api/endpoints/models';
+import { modelIdToOnnxModelField } from 'features/nodes/util/modelIdToOnnxModelField';
 
 export const MODEL_TYPE_MAP = {
   'sd-1': 'Stable Diffusion 1.x',
@@ -31,6 +35,7 @@ const ModelSelect = () => {
   const { currentModel } = useAppSelector(selector);
 
   const { data: mainModels, isLoading } = useGetMainModelsQuery();
+  const { data: onnxModels, isLoading: onnxLoading } = useGetOnnxModelsQuery();
 
   const data = useMemo(() => {
     if (!mainModels) {
@@ -50,16 +55,30 @@ const ModelSelect = () => {
         group: MODEL_TYPE_MAP[model.base_model],
       });
     });
+    forEach(onnxModels?.entities, (model, id) => {
+      if (!model) {
+        return;
+      }
+
+      data.push({
+        value: id,
+        label: model.model_name,
+        group: MODEL_TYPE_MAP[model.base_model],
+      });
+    });
 
     return data;
-  }, [mainModels]);
+  }, [mainModels, onnxModels]);
 
   const selectedModel = useMemo(
     () =>
       mainModels?.entities[
         `${currentModel?.base_model}/main/${currentModel?.model_name}`
+      ] ||
+      onnxModels?.entities[
+        `${currentModel?.base_model}/onnx/${currentModel?.model_name}`
       ],
-    [mainModels?.entities, currentModel]
+    [mainModels?.entities, onnxModels?.entities, currentModel]
   );
 
   const handleChangeModel = useCallback(
@@ -67,14 +86,16 @@ const ModelSelect = () => {
       if (!v) {
         return;
       }
-
+      let modelField = modelIdToMainModelField(v);
-      const modelField = modelIdToMainModelField(v);
+      if (v.includes('onnx')) {
+        modelField = modelIdToOnnxModelField(v);
+      }
       dispatch(modelSelected(modelField));
     },
     [dispatch]
   );
 
-  return isLoading ? (
+  return isLoading || onnxLoading ? (
     <IAIMantineSelect
       label={t('modelManager.model')}
       placeholder="Loading..."

invokeai/frontend/web/src/features/ui/components/tabs/ModelManager/subpanels/ModelManagerPanel/ModelList.tsx

@@ -50,7 +50,7 @@ const ModelList = () => {
 
   const [searchText, setSearchText] = useState<string>('');
   const [isSelectedFilter, setIsSelectedFilter] = useState<
-    'all' | 'ckpt' | 'diffusers'
+    'all' | 'ckpt' | 'diffusers' | 'olive'
   >('all');
   const [_, startTransition] = useTransition();
 

invokeai/frontend/web/src/services/api/endpoints/models.ts

@@ -5,6 +5,7 @@ import {
   ControlNetModelConfig,
   LoRAModelConfig,
   MainModelConfig,
+  OnnxModelConfig,
   TextualInversionModelConfig,
   VaeModelConfig,
 } from 'services/api/types';
@@ -13,6 +14,8 @@ import { ApiFullTagDescription, LIST_TAG, api } from '..';
 
 export type MainModelConfigEntity = MainModelConfig & { id: string };
 
+export type OnnxModelConfigEntity = OnnxModelConfig & { id: string };
+
 export type LoRAModelConfigEntity = LoRAModelConfig & { id: string };
 
 export type ControlNetModelConfigEntity = ControlNetModelConfig & {
@@ -27,6 +30,7 @@ export type VaeModelConfigEntity = VaeModelConfig & { id: string };
 
 type AnyModelConfigEntity =
   | MainModelConfigEntity
+  | OnnxModelConfigEntity
   | LoRAModelConfigEntity
   | ControlNetModelConfigEntity
   | TextualInversionModelConfigEntity
@@ -35,6 +39,10 @@ type AnyModelConfigEntity =
 const mainModelsAdapter = createEntityAdapter<MainModelConfigEntity>({
   sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
+
+const onnxModelsAdapter = createEntityAdapter<OnnxModelConfigEntity>({
+  sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
+});
 const loraModelsAdapter = createEntityAdapter<LoRAModelConfigEntity>({
   sortComparer: (a, b) => a.model_name.localeCompare(b.model_name),
 });
@@ -72,6 +80,38 @@ const createModelEntities = <T extends AnyModelConfigEntity>(
 
 export const modelsApi = api.injectEndpoints({
   endpoints: (build) => ({
+    getOnnxModels: build.query<EntityState<OnnxModelConfigEntity>, void>({
+      query: () => ({ url: 'models/', params: { model_type: 'onnx' } }),
+      providesTags: (result, error, arg) => {
+        const tags: ApiFullTagDescription[] = [
+          { id: 'OnnxModel', type: LIST_TAG },
+        ];
+
+        if (result) {
+          tags.push(
+            ...result.ids.map((id) => ({
+              type: 'OnnxModel' as const,
+              id,
+            }))
+          );
+        }
+
+        return tags;
+      },
+      transformResponse: (
+        response: { models: OnnxModelConfig[] },
+        meta,
+        arg
+      ) => {
+        const entities = createModelEntities<OnnxModelConfigEntity>(
+          response.models
+        );
+        return onnxModelsAdapter.setAll(
+          onnxModelsAdapter.getInitialState(),
+          entities
+        );
+      },
+    }),
     getMainModels: build.query<EntityState<MainModelConfigEntity>, void>({
       query: () => ({ url: 'models/', params: { model_type: 'main' } }),
       providesTags: (result, error, arg) => {
@@ -243,6 +283,7 @@ export const modelsApi = api.injectEndpoints({
 
 export const {
   useGetMainModelsQuery,
+  useGetOnnxModelsQuery,
   useGetControlNetModelsQuery,
   useGetLoRAModelsQuery,
   useGetTextualInversionModelsQuery,

invokeai/frontend/web/src/services/api/models/ONNXStableDiffusion1ModelConfig.ts

@@ -12,7 +12,6 @@ export type ONNXStableDiffusion1ModelConfig = {
   type: 'onnx';
   path: string;
   description?: string;
-  model_format: null;
   error?: ModelError;
   variant: ModelVariantType;
 };

invokeai/frontend/web/src/services/api/schema.d.ts

@@ -1174,7 +1174,7 @@ export type components = {
        * @description The nodes in this graph
        */
       nodes?: {
-        [key: string]: (components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
+        [key: string]: (components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"]) | undefined;
       };
       /**
        * Edges 
@@ -2904,6 +2904,34 @@ export type components = {
        */
       model: components["schemas"]["MainModelField"];
     };
+    /**
+     * OnnxModelLoaderInvocation 
+     * @description Loads an onnx model, outputting its submodels.
+     */
+    OnnxModelLoaderInvocation: {
+      /**
+       * Id 
+       * @description The id of this node. Must be unique among all nodes.
+       */
+      id: string;
+      /**
+       * Is Intermediate 
+       * @description Whether or not this node is an intermediate node. 
+       * @default false
+       */
+      is_intermediate?: boolean;
+      /**
+       * Type 
+       * @default onnx_model_loader 
+       * @enum {string}
+       */
+      type?: "onnx_model_loader";
+      /**
+       * Model 
+       * @description The model to load
+       */
+      model: components["schemas"]["OnnxModelField"];
+    };
     /**
      * MaskFromAlphaInvocation 
      * @description Extracts the alpha channel of an image as a mask.
@@ -4727,7 +4755,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"]  | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {
@@ -4764,7 +4792,7 @@ export type operations = {
     };
     requestBody: {
       content: {
-        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
+        "application/json": components["schemas"]["LoadImageInvocation"] | components["schemas"]["ShowImageInvocation"] | components["schemas"]["ImageCropInvocation"] | components["schemas"]["ImagePasteInvocation"] | components["schemas"]["MaskFromAlphaInvocation"] | components["schemas"]["ImageMultiplyInvocation"] | components["schemas"]["ImageChannelInvocation"] | components["schemas"]["ImageConvertInvocation"] | components["schemas"]["ImageBlurInvocation"] | components["schemas"]["ImageResizeInvocation"] | components["schemas"]["ImageScaleInvocation"] | components["schemas"]["ImageLerpInvocation"] | components["schemas"]["ImageInverseLerpInvocation"] | components["schemas"]["ControlNetInvocation"] | components["schemas"]["ImageProcessorInvocation"] | components["schemas"]["MainModelLoaderInvocation"] | components["schemas"]["OnnxModelLoaderInvocation"] | components["schemas"]["LoraLoaderInvocation"] | components["schemas"]["VaeLoaderInvocation"] | components["schemas"]["MetadataAccumulatorInvocation"] | components["schemas"]["DynamicPromptInvocation"] | components["schemas"]["CompelInvocation"] | components["schemas"]["ClipSkipInvocation"] | components["schemas"]["AddInvocation"] | components["schemas"]["SubtractInvocation"] | components["schemas"]["MultiplyInvocation"] | components["schemas"]["DivideInvocation"] | components["schemas"]["RandomIntInvocation"] | components["schemas"]["ParamIntInvocation"] | components["schemas"]["ParamFloatInvocation"] | components["schemas"]["TextToLatentsInvocation"] | components["schemas"]["LatentsToImageInvocation"] | components["schemas"]["ResizeLatentsInvocation"] | components["schemas"]["ScaleLatentsInvocation"] | components["schemas"]["ImageToLatentsInvocation"] | components["schemas"]["CvInpaintInvocation"] | components["schemas"]["RangeInvocation"] | components["schemas"]["RangeOfSizeInvocation"] | components["schemas"]["RandomRangeInvocation"] | components["schemas"]["ImageCollectionInvocation"] | components["schemas"]["FloatLinearRangeInvocation"] | components["schemas"]["StepParamEasingInvocation"] | components["schemas"]["NoiseInvocation"] | components["schemas"]["UpscaleInvocation"] | components["schemas"]["RestoreFaceInvocation"] | components["schemas"]["InpaintInvocation"] | components["schemas"]["InfillColorInvocation"] | components["schemas"]["InfillTileInvocation"] | components["schemas"]["InfillPatchMatchInvocation"] | components["schemas"]["GraphInvocation"] | components["schemas"]["IterateInvocation"] | components["schemas"]["CollectInvocation"] | components["schemas"]["CannyImageProcessorInvocation"] | components["schemas"]["HedImageProcessorInvocation"] | components["schemas"]["LineartImageProcessorInvocation"] | components["schemas"]["LineartAnimeImageProcessorInvocation"] | components["schemas"]["OpenposeImageProcessorInvocation"] | components["schemas"]["MidasDepthImageProcessorInvocation"] | components["schemas"]["NormalbaeImageProcessorInvocation"] | components["schemas"]["MlsdImageProcessorInvocation"] | components["schemas"]["PidiImageProcessorInvocation"] | components["schemas"]["ContentShuffleImageProcessorInvocation"] | components["schemas"]["ZoeDepthImageProcessorInvocation"] | components["schemas"]["MediapipeFaceProcessorInvocation"] | components["schemas"]["LeresImageProcessorInvocation"] | components["schemas"]["TileResamplerProcessorInvocation"] | components["schemas"]["SegmentAnythingProcessorInvocation"] | components["schemas"]["LatentsToLatentsInvocation"];
       };
     };
     responses: {

invokeai/frontend/web/src/services/api/types.d.ts

@@ -30,6 +30,7 @@ export type OffsetPaginatedResults_ImageDTO_ =
 export type ModelType = components['schemas']['ModelType'];
 export type BaseModelType = components['schemas']['BaseModelType'];
 export type MainModelField = components['schemas']['MainModelField'];
+export type OnnxModelField = components['schemas']['OnnxModelField'];
 export type VAEModelField = components['schemas']['VAEModelField'];
 export type LoRAModelField = components['schemas']['LoRAModelField'];
 export type ModelsList = components['schemas']['ModelsList'];
@@ -47,6 +48,7 @@ export type MainModelConfig =
   | components['schemas']['StableDiffusion1ModelDiffusersConfig']
   | components['schemas']['StableDiffusion2ModelCheckpointConfig']
   | components['schemas']['StableDiffusion2ModelDiffusersConfig'];
+export type OnnxModelConfig = components['schemas']['ONNXStableDiffusion1ModelConfig']
 export type AnyModelConfig =
   | LoRAModelConfig
   | VaeModelConfig
@@ -107,6 +109,9 @@ export type ImageCollectionInvocation = TypeReq<
 export type MainModelLoaderInvocation = TypeReq<
   components['schemas']['MainModelLoaderInvocation']
 >;
+export type OnnxModelLoaderInvocation = TypeReq<
+  components['schemas']['OnnxModelLoaderInvocation']
+>;
 export type LoraLoaderInvocation = TypeReq<
   components['schemas']['LoraLoaderInvocation']
 >;